A wake-up call for the engineering and biomedical science communities
Bibliographic record
Abstract
With the burst of Information Technology (IT) bubble at the beginning of this century, people are looking for the next wave of technology in which to invest. While we believe that biomedical applications and systems are this next stage, unfortunately, the engineering and bioscience communities are unprepared for the many challenges. In order to connect the engineering and the biomedical science communities, we established the LifeScience Systems and Applications (LiSSA) Technical Committee within IEEE Circuits and Systems Society in 2005--an initiative supported by the National Institutes of Health (NIH) through a conference grant to enable dialogue between the engineering and biomedical science communities. Henceforth, we have organized several annual workshops with different themes on the NIH campus. After each workshop, a white paper is published in IEEE circuits and systems magazine to present the major challenges in various chosen theme areas. Recently, we chose "Biomarker Development and Applications" as our workshop theme. For the first time, we invited eight IEEE societies and various NIH institutes to send their representatives for face-to-face dialogue. This article presents the major challenges in biomarker development and applications based on the general consensus of the conference. The aim of the article is to serve as a wake-up call for more engineers to participate in crucial life-science application and systems research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.036 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.015 | 0.025 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.043 | 0.052 |
| Insufficient payload (model declined to judge) | 0.037 | 0.023 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".